Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 3-4, 6, and 10 are objected to because of the following
informalities.
• Claim 1, line 10 “the assessments” should read “the pre-determined AI-guided assessments”.
• Claim 1, line 13, “initial evaluation response output” should read “the initial evaluation response output”.
• Claim 1, line 15, “the assessments movement video” should read “the movement video”.
• Claim 1, line 19, “movement database” should read “the movement database”.
• Claim 1, line 20, “initial evaluation database” should read “the initial evaluation database”.
• Claim 1, line 24, “user” should read “the user”.
• Claim 1, line 25, “the one or more movements” should read “one or more movements”.
• Claim 1, line 35, “the system” should read “a system”.
• Claim 1, line 39, “the feedback” should read “the real-time feedback”.
• Claim 3, line 1, “the modeling algorithm” should read “a modeling algorithm”.
• Claim 3, line 2, “the movement analysis engine” should read “a movement analysis engine”.
• Claim 4, line 1, “the server” should read “a server”.
• Claim 6, line 1, “one or more physiological sensors” should read “the one or more physiological sensors”.
• Claim 6, line 2, “the physiological sensors” should read “the one or more physiological sensors”.
• Claim 10, line 4, “the user profile” should read “a user profile”.
• Claim 10, lines 6-7, “the biomechanical data” should read “biomechanical data”.
• Claim 10, line 7, “the performed exercise” should read “a performed exercise”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1: Does the claimed invention fall inside one of the four statutory categories (process, machine, manufacture, or composition of matter)? Yes for claims 1-10. Claims 1-9 are drawn to an artificial intelligence apparatus for prescription of exercise routines (i.e., a manufacture). Claim 10 is drawn to a method for an AI-generated performance progress note summary report (i.e., a process).
Step 2A - Prong One: Do the claims recite a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon)? Yes, for claims 1-10.
Claim 1 recites:
An artificial intelligence apparatus for prescription of exercise routines to a user;
comprising: a memory configured to store one or more inputs and user data, wherein the memory is communicatively coupled to a network;
a processor communicatively coupled to the network, configured to: provide access to the user to complete an initial evaluation comprising a general health and performance questionnaires and initial diagnosis;
provide an access to the user to perform pre-determined AI-guided assessments, wherein a camera and one or more physiological sensors are communicatively coupled to allows user to access the assessments, to perform AI-driven assessment of joints range of motion, muscular strength and performance to generate an initial evaluation response output;
receive initial evaluation response output that comprises: a movement video and physiological data from a user for the pre-determined AI-guided assessments;
analyze the assessments movement video and physiological data with AI-module with respect to a movement database, initial evaluation database, and extracting one or more pre-determined movement parameters;
prescribe one or more exercise routines to the user, wherein an AI-prescription established with respect to movement database, movement assessments, other users progress notes, exercise database and initial evaluation database;
provide information to the user about a one or more AI-prescribed exercise routine that can be accessed via the network;
transmit to an user interface that comprises a display and a camera, wherein the display allows user to access the AI-prescribed exercise routine and the camera is configured to capture video data of the one or more movements of the user and the physiological sensor is configurated to collect performance data while participating in the exercise routine;
analyze the one or more movements with respect to movement and initial evaluation databases and one or more pre-determined movement parameters;
determine the performance of AI-prescribed movements of the user with respect to the exercise and initial evaluation databases movement parameters;
generate and provide real-time feedback to the user based upon the performance of the movement, wherein the real time feedback comprising: an automated response from the AI-module;
real-time instruction using AI-driving module from the system comparing a measured movement parameter to a target movement parameter.
a summary of exercise performance based upon the movement of the user and analysis of the user movement or group of users, wherein the feedback includes at least one of the following: visual, auditory, or haptic modalities.
These steps amount to a form of mental process and organizing human activity (i.e., an abstract idea) because a human can record user data, provide initial evaluations and assessments, analyze initial evaluations and assessments, and prescribe exercise routines based on these data. “Often physical therapists or trainers print exercises on a paper, give verbal or video prescription of exercise” [0004].
Independent claim 10 describes steps that are similar to steps of claim 1 (and therefore recite limitations that fall within this subject matter of grouping abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Dependent claims 2-9 are directed towards mini-tasks (monitoring physiological metrics, storing baseline measurements, summarizing exercise performance, etc.) for an artificial intelligence apparatus for prescription of exercise routines. Each claim amounts to a form of collecting, generating, and analyzing information, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). As such, the Examiner concludes that claims 2-9 recite an abstract idea.
Step 2A – Prong Two: Do the claims recite additional elements that integrate the exception into a practical application of the exception? No
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using memory and processors (independent claims 1 and 10 and dependent claims 2-9) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of memory and processors (independent claims 1 and 10 and dependent claims 2-9) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, they serve to limit the application of the abstract idea to a computerized environment (e.g., identifying and displaying, etc.) performed by a computing device, processor, and memory, etc. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-9 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective independent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? i.e., Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea? No
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong Two”, the identified additional elements in independent claims 1 and 10 and dependent claims 2-9 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Dependent claims 2-9 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. Therefore, claims 1-10 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable under US 20220401794 A1 (“Bissonnette”) in view of US 20220020469 A1 (“Tanner”).
In regards to claim 1, Bissonnette discloses the following limitations with the exception of the underlined limitations.
An artificial intelligence apparatus for prescription of exercise routines to a user ([0002], “the present disclosure relates to using artificial intelligence to … create an exercise program”);
comprising: a memory configured to store one or more inputs and user data, wherein the memory is communicatively coupled to a network ([0113], “the exercise machine … may include … memory devices, and network interface devices”);
a processor communicatively coupled to the network, configured to ([0113], “the exercise machine … may include one or more processing devices … and network interface devices.”): provide access to the user to complete an initial evaluation comprising a general health and performance questionnaires and initial diagnosis;
provide an access to the user to perform pre-determined AI-guided assessments, wherein a camera ([0239], “The computer system ... may be ... a camera”) and one or more physiological sensors are communicatively coupled to allows user to access the assessments, to perform AI-driven assessment of joints range of motion, muscular strength and performance to generate an initial evaluation response output ([0110], “Using various performance measurements from one or more sensors, attributes of users of the exercise machine, user-reported difficulty levels of exercises, user-reported pain levels, and the like, the user energy consumption metrics may be objectively monitored and/or measured.”);
receive initial evaluation response output that comprises: a movement video and physiological data from a user for the pre-determined AI-guided assessments ([0123], “the ... data may include various inputs ... (e.g., age, weight, height, gender, procedures performed, condition of user, goals for outcomes of exercising, etc.), performance measurements, and the like) and mapped outputs. The mapped outputs may include ... multimedia (e.g., video/audio) ... segments for a virtual coach to ... video ... to be presented on the user interface”);
analyze the assessments movement video ([0123], “The mapped outputs may include ... multimedia (e.g., video/audio) ... segments”) and physiological data with AI-module ([0123], “the ... data may include various inputs ... (e.g., age, weight, height, gender, procedures performed, condition of user, goals for outcomes of exercising, etc.)”) with respect to a movement database, initial evaluation database ([0126], “The data source … may be a relational database”), and extracting one or more pre-determined movement parameters ([0126], “The data source may include exercises, ... weights, and/or parameters used to configure ... an exercise schedule”);
prescribe one or more exercise routines to the user, wherein an AI-prescription established with respect to movement database, movement assessments, other users progress notes, exercise database and initial evaluation database ([0197], “The exercise plan may be generated by artificial intelligence and/or prescribed by a doctor, a physical therapist, or any other qualified clinician”);
provide information to the user about a one or more AI-prescribed exercise routine that can be accessed via the network ([0116], “The exercise plan specific to a patient may be transmitted via the network”);
transmit to an user interface that comprises a display and a camera, wherein the display allows user to access the AI-prescribed exercise routine and the camera is configured to capture video data of the one or more movements of the user ([0239], “The computer system ... may be capable of executing the application ... and presenting the user interface ... The computer system ... may be ... a camera, a video camera”) and the physiological sensor is configurated to collect performance data while participating in the exercise routine ([0110], “Using various performance measurements from one or more sensors, attributes of users of the exercise machine ... may be ... monitored and/or measured”);
analyze the one or more movements with respect to movement ([0282], “Based on the types of movements ... the disclosed embodiments may determine ... user ... score”) and initial evaluation databases ([0126], “The data source … may be a relational database”) and one or more pre-determined movement parameters ([0126], “The data source may include exercises, ... weights, and/or parameters used to configure ... an exercise schedule”);
determine the performance of AI-prescribed movements of the user with respect to the exercise and initial evaluation databases movement parameters ([0123], “the ... data may include ... performance measurements ... and mapped outputs. ... The ... mapped outputs may ... include ... a virtual coach to speak ... The virtual coach may be driven and controlled by artificial intelligence”);
generate and provide real-time feedback to the user based upon the performance of the movement, wherein the real time feedback comprising: an automated response from the AI-module ([0081], “The subject matter disclosed herein relates to a control system for an exercise machine ... capable of using predetermined thresholds or dynamically calculating them, such that the person using the machine can be immediately informed through real-time visual and/or other sensorial feedback” Examiner notes that sensory feedback can be an automated response.);
real-time instruction using AI-driving module from the system comparing a measured movement parameter to a target movement parameter ([0036], “FIG. 27A illustrates an example method for generating, using a machine learning model, an exercise session for a user and causing a virtual coach to provide instructions pertaining to the exercise session”).
a summary of exercise performance based upon the movement of the user and analysis of the user movement or group of users ([0087], “when the exercise plan is complete, the control system may generate a performance report that presents various information”), wherein the feedback includes at least one of the following: visual, auditory, or haptic modalities ([0147], “The control system may provide various visual, audio, and/or haptic feedback”).
Tanner discloses
provide access to the user to complete an initial evaluation comprising a general health and performance questionnaires and initial diagnosis ([0095], “Table 1 ... provides ... pre-evaluation standardized questionnaire”);
and initial evaluation ([0095], “Table 1 ... provides ... pre-evaluation standardized questionnaire”)
Bissonnette and Tanner are considered analogous to the claimed invention
because they are in the same field of exercise programs and physical therapy. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for an artificial intelligence apparatus for prescription of exercise routines to a user; comprising: a memory configured to store one or more inputs and user data, wherein the memory is communicatively coupled to a network; a processor communicatively coupled to the network, configured to: provide an access to the user to perform pre-determined AI-guided assessments, wherein a camera and one or more physiological sensors are communicatively coupled to allows user to access the assessments, to perform AI-driven assessment of joints range of motion, muscular strength and performance to generate an initial evaluation response output; receive initial evaluation response output that comprises: a movement video and physiological data from a user for the pre-determined AI-guided assessments; analyze the assessments movement video and physiological data with AI-module with respect to a movement database, initial evaluation database, and extracting one or more pre-determined movement parameters; prescribe one or more exercise routines to the user, wherein an AI-prescription established with respect to movement database, movement assessments, other users progress notes, exercise database and initial evaluation database; provide information to the user about a one or more AI-prescribed exercise routine that can be accessed via the network; transmit to an user interface that comprises a display and a camera, wherein the display allows user to access the AI-prescribed exercise routine and the camera is configured to capture video data of the one or more movements of the user and the physiological sensor is configurated to collect performance data while participating in the exercise routine; analyze the one or more movements with respect to movement databases; determine the performance of AI-prescribed movements of the user with respect to the exercise and initial evaluation databases movement parameters; generate and provide real-time feedback to the user based upon the performance of the movement, wherein the real time feedback comprising: an automated response from the AI-module; real-time instruction using AI-driving module from the system comparing a measured movement parameter to a target movement parameter, a summary of exercise performance based upon the movement of the user and analysis of the user movement or group of users, wherein the feedback includes at least one of the following: visual, auditory, or haptic modalities, as disclosed by Bissonnette, provide access to the user to complete an initial evaluation comprising a general health and performance questionnaires and initial diagnosis; and initial evaluation, as disclosed by Tanner, to provide a pre-evaluation standardized questionnaire for remote physical therapy or training using feedback.
In regards to claim 2, Bissonnette discloses
a secondary device for monitoring physiological metrics of the user ([0234], “the processing device may monitor a parameter associated with the user … The parameter may include a vital sign (e.g., heartrate, blood pressure)” Examiner notes that physiological metrics include vital signs.).
In regards to claim 3, Bissonnette discloses
wherein the modeling algorithm ([0032], “FIG. 23 illustrates … an exercise session determined for a user by a machine learning model” Examiner notes that a machine learning model is guided by algorithms.) comprises a human pose estimation model for each movement for the movement analysis engine ([0263], “the set of exercises may be selected based on different types of exercises … that focus on different parts of a user's body or improvements thereto, different movements, and the like” Examiner notes that human poses can include different movements.).
In regards to claim 4, Bissonnette discloses
wherein the server can store one or more baseline measurements for each movement for the user and one or more prescribed measurements parameters for each movement ([0119], “The servers … may store profiles for each of the users that use the exercise machine … The profiles may include information about the users such as … one or more exercise plans”).
In regards to claim 5, Bissonnette discloses
wherein the summary of exercise performance includes at least one of the following ([0087], “when the exercise plan is complete, the control system may generate a performance report that presents various information”): Joint range of motion, lift height, depth, speed of movement, joint displacement ([0123], “the training data may include … range of motion of users”), standing and walking performance scales ([0272], “the physical activity goal may include activities aimed at … walking … standing without pain”).
In regards to claim 6, Bissonnette discloses
further comprising one or more physiological sensors, wherein the physiological sensors monitors at least one of the following: heart rate, energy exertion, number of steps, blood pressure, and blood oxygen ([0196], “the biometric sensor can comprise position sensors located on the user” Examiner notes that a biometric sensor can monitor heart rate, energy exertion, number of steps, blood pressure, and blood oxygen.).
In regards to claim 7, Bissonnette discloses
further comprising a social network among users wherein the social network provides at least the following: communication between users, exchanging comment, sharing video and picture, interacting with the instructor, and observing the performance and ratings of other users ([0246], “the computing device … may … use an application programming interface …, such as … a social network system” Examiner notes that social network systems provide communication between users, allowing them to exchange comments, share videos and pictures, interact with instructors, and observe the performance and ratings of other users.).
In regards to claim 8, Bissonnette discloses
wherein the summary of exercise performance transmits to databases ([0126], “The data source … may be a relational database”) via a network ([0116], “The exercise plan specific to a patient may be transmitted via the network”).
In regards to claim 9, Bissonnette discloses
wherein the modeling algorithm comprises a natural language processing for ([0197], “The exercise plan may be generated using artificial intelligence via one or more trained machine learning models” Examiner notes that machine learning models use algorithms, which can use natural language processing.) AI-generated summarized progress note report ([0176], “the control system may generate a performance report that presents various information”).
In regards to claim 10, Bissonnette discloses
a method for an AI-generated post-performance progress note summary report, comprising ([0176], “the control system may generate a performance report that presents various information”): a memory having one or more prescribed movement programs and/or classes ([0113], “the exercise machine … may include … memory devices, and network interface devices”), wherein the movement programs can include the user profile, one or more movements ([0123], “the ... data may include various inputs ... (e.g., age, weight, height, gender, procedures performed, condition of user, goals for outcomes of exercising, etc.)”) and one or more movement parameters, and one or more users ([0126], “The data source may include exercises, ... weights, and/or parameters used to configure ... an exercise schedule”);
the AI-generated summarized feedback generates a performance report ([0087], “the control system may generate a performance report that presents various information”) based upon the biomechanical data of a user or multi users’ performance of the performed exercise ([0123], “the ... data may include various ... performance measurements, and the like” Examiner notes that biomechanical data is data associated with movement.).
providing post-performance AI-generated summarized feedback report to the user upon completing the AI-prescribed exercise routine ([0087], “the control system may generate a performance report that presents various information”);
and communicating the post-performance feedback to the exercise and initial evaluation databases ([0113], “the exercise machine … may include one or more processing devices” Examiner notes that processing devices facilitate database management, which involves organizing, storing, manipulating, and retrieving data.).
Contact Information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Lisa Antoine whose telephone number is
(571) 272-4252 and whose email address is lantoine@uspto.gov. The examiner can be reached Monday-Thursday, 7:30 am – 5:30 pm CT. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai, can be reached on (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/LISA H ANTOINE/
Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715